922769_zh-TW

liliconcom

基於方程變數的JSL動態約束最小化方程

I am using Constrained Minimize() in JSL and would like to build both the 目標函數 and the 動態設定約束集, based on which variables appear in the objective equation.

在簡單的情況下，我可以把所有內容都硬編碼進去，例如：

R_nom = 0.1;
L_nom = 0.5;
S_nom = 48.2;
B_nom = 0.05;
R_eq = 0.1;
L_eq = 0.5;
S_eq = 48.2;
B_eq = 0.05;
R_sd = :sd_R;
L_sd = :sd_L;
S_sd = :sd_S;
B_sd = :sd_B;
bias_const = 1.5;
R_bias = R_sd * bias_const;
L_bias = L_sd * bias_const;
S_bias = S_sd * bias_const;
B_bias = B_sd * bias_const;




minFun = Constrained Minimize(
	(16.388 * Pi() * R_nom ^ 2 * L_nom - B_nom) * S_nom,
	{R_nom( R_eq - R_Bias, R_eq + R_Bias ), L_nom( L_eq - L_Bias, L_eq + L_Bias ), S_nom( S_eq - S_Bias, S_eq + S_Bias ),
	B_nom( B_eq - B_Bias, B_eq + B_Bias )},
	<
     <TOLERANCE></TOLERANCE>

<P>然而，<STRONG data-start="1086" data-end="1118">該等式可能會動態變化</STRONG>，例如：</P>

<CODE class=" language-jsl">R_nom^2 * L_nom // instead of 16.388 * Pi() * R_nom ^ 2 * L_nom - B_nom) * S_nom

In that case, I only want constraints for R_nom and L_nom, not for B_nom or S_nom.

我想要達成的目標

1. Define the objective function as an expression (Expr()).

2. 確定方程中使用的輸入變數（我已經為此創建了一個函數）。

   varsUsed = {"R_nom","L_nom"}; //when equation is R_nom^2 * L_nom

   
   
   

3. 自動僅為這些變數建立約束列表。

4. Pass the dynamically generated constraint list into Constrained Minimize().

目前方法

我將參數元資料（名目變數、平衡值、偏差）儲存在關聯數組中，例如：

paramInfo = Associative Array();

paramInfo["R_nom"] = {R_nom, R_eq, R_bias};
paramInfo["L_nom"] = {L_nom, L_eq, L_bias};
paramInfo["S_nom"] = {S_nom, S_eq, S_bias};
paramInfo["B_nom"] = {B_nom, B_eq, B_bias};

我被困住了

I am having trouble 動態建構有效的約束表達式 in JSL so that they are accepted by Constrained Minimize().

<LINGO-LABS id="lingo-labs-922769" slang="en-US" mode="CREATE"> <LINGO-LABEL>自動化和腳本編寫</LINGO-LABEL> </LINGO-LABS> 回覆：基於方程變數的JSL動態約束最小化方程

您很可能需要進行表達式求值，並混合使用名稱表達式。這或許能給您一些啟發。

Names Default To Here(1);

params = Associative Array();
params["R_nom"] = {R_nom, R_eq, R_bias};
params["L_nom"] = {L_nom, L_eq, L_bias};
params["S_nom"] = {S_nom, S_eq, S_bias};
params["B_nom"] = {B_nom, B_eq, B_bias};

vars = {"R_nom","L_nom"}; 


l = {};
For Each({var}, vars,
	e = Substitute(Expr(nom(eq - bias, eq + bias)),
		Expr(nom), Name Expr(params[var][1]),
		Expr(eq), Name Expr(params[var][2]),
		Expr(bias), Name Expr(params[var][3]),
	);
	Insert Into(l, Name Expr(e));
);


minFun = EvalExpr(
	Constrained Minimize(
		(16.388 * Pi() * R_nom ^ 2 * L_nom - B_nom) * S_nom,
		Expr(l),
		<
     <TOLERANCE></TOLERANCE>